Optimizing DaaS Web Service Based Data Mashups

Barhamgi, Mahmoud; Ghédira, Chirine; Benslimane, Djamal; Tbahriti, Salah-Eddine; Mrissa, Michaël

doi:10.1109/scc.2011.72

Cited by 7 publications

(10 citation statements)

References 15 publications

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“…Assume also two data services: In contrast, both of S 1 and S 2 are usable for Q, to cover as much as possible of the potential values of x. Our composition algorithm extends the previous ones with: (i ) a probabilistic subsumption test to determine in a polynomial time the minimum number of services required to satisfy the value constraints that may be specified on query's parameters [6], and (ii ) a mechanism to optimize the generated composition plans based on value constraints specified in service descriptions [7].…”

Section: Service-based Query Resolutionmentioning

confidence: 99%

“…We summarize below obtained results 5 : Composition construction and execution : Our experiments in [7] showed that our composition algorithm can handle hundreds of data services in a reasonable time. For example, for chain queries [13] and RPVs with a length of 3 or 4 object properties the algorithm was able to handle up to 400 services in less than 4 seconds.In the context of parameterized queries, our experiments in [6] showed that our algorithm to find the minimum set of services introduced only a small cost at the composition construction time (i.e., in all experiments the algorithm required less than 10% of the time needed to rewrite the query), and improved substantially the composition execution time (i.e., in all experiments the composition execution time was reduced to less than 0.75% of the time needed without optimization), as it removes redundant services. In the context of preferences queries, our experiments in [9] considered that services can be grouped in classes.…”

Section: Implementation and Evaluationmentioning

confidence: 99%

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Pairse

et al. 2013

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Privacy is among the key challenges to data integration in many sectors, including healthcare, e-government, etc. The PAIRSE project aims at providing a flexible, looselycoupled and privacy-preserving data integration system in P2P environments. The project exploits recent Web standards and technologies such as Web services and ontologies to export data from autonomous data providers as reusable services, and proposes the use of service composition as a viable solution to answer data integration needs on the fly. The project proposed new composition algorithms and service/composition execution models that preserve privacy of data manipulated by services and compositions. The proposed integration system was demonstrated at EDBT 2013 and VLDB 2011.

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Section: Service-based Query Resolutionmentioning

confidence: 99%

Section: Implementation and Evaluationmentioning

confidence: 99%

Pairse

et al. 2013

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“…It is a special class of mashup application that combines information on the fly from multiple data sources. Its applications vary from addressing transient business needs in modern enterprises to conducting scientific research in e-science communities [1]. The data sources are provided through Web Services, also known as DaaS (Data-as-a-Service) or Data Service [2,3].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Data Maintenance Strategy for Data Service Mashup Based on Materialized View Selection

Zhang

Han

Wang

2013

Service-Oriented Computing - ICSOC 2012 Workshops

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Abstract. While end-users enjoy the full-fledged data service mashup with high convenience and flexibility, such issues as the response efficiency and the maintenance cost have popped up as the major concerns. In this paper, an efficient data maintenance strategy for the data service mashup is proposed. The strategy proposes a data maintenance model to measure the response cost and update cost of a group of data service mashups in terms of the request frequency and update frequency. Based on the model, a materialized view selection for data service mashup is proposed. Experiments show that our strategy can effectively reduce the maintenance cost of a lot of hosted data service mashups.

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“…An important characteristic for the enterprises of such a collaborative environment is the ability to employ data sharing with one another. By semantically annotating a DPS, it is able to be semantically discovered, mediated, and composed.Current means of development for annotations for DPSs are proposed as an afterthought to existing services [7,9,12]. One potential solution is to provide filtered data through access control.…”

mentioning

confidence: 99%

“…A domain ontology is a shared, formal description of data that is supported by computational logic for inference; resource description framework schema (RDFS) [10] and Web Ontology Language (OWL) [11] are two potential representations for ontologies on the Web. By semantically annotating a DPS, it is able to be semantically discovered, mediated, and composed.Current means of development for annotations for DPSs are proposed as an afterthought to existing services [7,9,12]. There are major issues with defining DPSs in this manner.…”

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confidence: 99%

Fine‐grained filtering to provide access control for data providing services within collaborative environments

Brown

Hayes

Allison

et al. 2013

Concurrency and Computation

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A data providing service (DPS) in service-oriented architecture is tasked only with the retrieval of data that are annotated over a domain ontology. One particular motivating application of DPSs is their use within collaborative environments. An important characteristic for the enterprises of such a collaborative environment is the ability to employ data sharing with one another. A major concern in this situation is the protection of each enterprise's privacy while still permitting data sharing. One potential solution is to provide filtered data through access control. This work describes how to implement access control through fine-grained filtering of DPS response messages; it is accomplished using a filtering ontology and relations between the domain ontology of DPS and the proposed filtering ontology. Therefore, enterprises can write enterprise-specific access control policies referencing a common filtering ontology defined within a collaborative environment, enabling access control-based data sharing within the environment. This work additionally illustrates the implementation of our general solution to data providing web services, interpreted by an eXtensible Access Control Markup Language-based access control framework. The implementation is further evaluated in a case study of real world data, provided by a health research institute in London, Canada.K. BROWN ET AL. product. Should data sharing be required within this domain, the data services must also provide some security measures, such as access control policies (ACPs). These policies should reference the shared concepts to maintain the privacy of each enterprise. This is not an easy task as each enterprise may have existing ACPs. The need for specialized data services is emphasized by Carey [5]. Carey outlines a very simplified architecture showing how data services are used by business processes in order to facilitate access to data objects while also suggesting that new methodologies and tools need to be developed for data service modeling as data services are likely to remain important to applications.The data service contract in non-semantic SOA only describes a service syntactically and thus requires human input to semantically interpret the contract. As a result of this, semantic extensions to SOA have been proposed that annotate conventional services to promote interoperability [6][7][8][9]. Services that specialize in retrieving data and are annotated with semantics are termed data providing services (DPSs). A DPS extends a conventional SOA service by providing a DPS profile that semantically annotates the service's contract using a parameterized view over a domain ontology. A domain ontology is a shared, formal description of data that is supported by computational logic for inference; resource description framework schema (RDFS) [10] and Web Ontology Language (OWL) [11] are two potential representations for ontologies on the Web. By semantically annotating a DPS, it is able to be semantically discovered, mediated, and composed.Current me...

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Optimizing DaaS Web Service Based Data Mashups

Cited by 7 publications

References 15 publications

Pairse

Pairse

An Efficient Data Maintenance Strategy for Data Service Mashup Based on Materialized View Selection

Fine‐grained filtering to provide access control for data providing services within collaborative environments

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